基于有序加权聚合的以人为中心的排序推荐

IF 3.8 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Applied System Innovation Pub Date : 2023-03-06 DOI:10.3390/asi6020036
S. S. Sohail, Asfia Aziz, R. Ali, S. H. Hasan, D. Madsen, M. Alam
{"title":"基于有序加权聚合的以人为中心的排序推荐","authors":"S. S. Sohail, Asfia Aziz, R. Ali, S. H. Hasan, D. Madsen, M. Alam","doi":"10.3390/asi6020036","DOIUrl":null,"url":null,"abstract":"In this paper, we propose an approach to recommender systems that incorporates human-centric aggregation via Ordered Weighted Aggregation (OWA) to prioritize the suggestions of expert rankers over the usual recommendations. We advocate for ranked recommendations where rankers are assigned weights based on their ranking position. Our approach recommends books to university students using linguistic data summaries and the OWA technique. We assign higher weights to the highest-ranked university to improve recommendation quality. Our approach is evaluated on eight parameters and outperforms traditional recommender systems. We claim that our approach saves storage space and solves the cold start problem by not requiring prior user preferences. Our proposed scheme can be applied to decision-making problems, especially in the context of recommender systems, and offers a new direction for human-specific task aggregation in recommendation research.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":" ","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Human-Centric Aggregation via Ordered Weighted Aggregation for Ranked Recommendation in Recommender Systems\",\"authors\":\"S. S. Sohail, Asfia Aziz, R. Ali, S. H. Hasan, D. Madsen, M. Alam\",\"doi\":\"10.3390/asi6020036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose an approach to recommender systems that incorporates human-centric aggregation via Ordered Weighted Aggregation (OWA) to prioritize the suggestions of expert rankers over the usual recommendations. We advocate for ranked recommendations where rankers are assigned weights based on their ranking position. Our approach recommends books to university students using linguistic data summaries and the OWA technique. We assign higher weights to the highest-ranked university to improve recommendation quality. Our approach is evaluated on eight parameters and outperforms traditional recommender systems. We claim that our approach saves storage space and solves the cold start problem by not requiring prior user preferences. Our proposed scheme can be applied to decision-making problems, especially in the context of recommender systems, and offers a new direction for human-specific task aggregation in recommendation research.\",\"PeriodicalId\":36273,\"journal\":{\"name\":\"Applied System Innovation\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2023-03-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied System Innovation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/asi6020036\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied System Innovation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/asi6020036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

在本文中,我们提出了一种推荐系统的方法,该方法通过有序加权聚合(OWA)结合以人为中心的聚合,将专家排名者的建议优先于通常的推荐。我们提倡排名推荐,其中排名者根据其排名位置分配权重。我们的方法是使用语言数据摘要和OWA技术向大学生推荐书籍。我们为排名最高的大学分配更高的权重,以提高推荐质量。我们的方法在八个参数上进行了评估,并且优于传统的推荐系统。我们声称,我们的方法节省了存储空间,解决了冷启动问题,不需要事先的用户偏好。该方法可以应用于决策问题,特别是推荐系统的决策问题,为推荐研究中针对人类的任务聚合提供了新的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Human-Centric Aggregation via Ordered Weighted Aggregation for Ranked Recommendation in Recommender Systems
In this paper, we propose an approach to recommender systems that incorporates human-centric aggregation via Ordered Weighted Aggregation (OWA) to prioritize the suggestions of expert rankers over the usual recommendations. We advocate for ranked recommendations where rankers are assigned weights based on their ranking position. Our approach recommends books to university students using linguistic data summaries and the OWA technique. We assign higher weights to the highest-ranked university to improve recommendation quality. Our approach is evaluated on eight parameters and outperforms traditional recommender systems. We claim that our approach saves storage space and solves the cold start problem by not requiring prior user preferences. Our proposed scheme can be applied to decision-making problems, especially in the context of recommender systems, and offers a new direction for human-specific task aggregation in recommendation research.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Applied System Innovation
Applied System Innovation Mathematics-Applied Mathematics
CiteScore
7.90
自引率
5.30%
发文量
102
审稿时长
11 weeks
期刊最新文献
Research on Chinese Nested Entity Recognition Based on IDCNNLR and GlobalPointer AI-Powered Academic Guidance and Counseling System Based on Student Profile and Interests Using Smart Traffic Lights to Reduce CO2 Emissions and Improve Traffic Flow at Intersections: Simulation of an Intersection in a Small Portuguese City Predictive Modeling of Light–Matter Interaction in One Dimension: A Dynamic Deep Learning Approach Project Management Efficiency Measurement with Data Envelopment Analysis: A Case in a Petrochemical Company
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1